International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1251
Статья научная
Manually, to manage stocks amounts spending the every day in the rays to count for each product the number which it remains in stores, or to record by a scanner head barcode information dependent of each product. However, the mission become increasingly difficult if several warehouses are found, that involves much time to pass from a product to another, moreover that requires agents to carry out these spots. In this article we use a network architecture neuron combined with the readers bar code of technology vision, this method allows to know in real time information concerning each product in stock. It will allow besides introducing the concept of real stocks rather than physical. However The basic classical use of data and to feed it will be completely changed by the spheres of knowledge which generates the NN (Neural Network) to store information on the quantity at a given time (Dynamic inventory), the entries(delivery of suppliers ) and the outputs ( delivery or sale with the customers and use of manufacturing pieces or repair ).
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Solving Economic Load Dispatch Problem Using Particle Swarm Optimization Technique
Статья научная
Economic load dispatch (ELD) problem is a common task in the operational planning of a power system, which requires to be optimized. This paper presents an effective and reliable particle swarm optimization (PSO) technique for the economic load dispatch problem. The results have been demonstrated for ELD of standard 3-generator and 6-generator systems with and without consideration of transmission losses. The final results obtained using PSO are compared with conventional quadratic programming and found to be encouraging.
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Solving Practical Economic Dispatch Problems Using Improved Artificial Bee Colony Method
Статья научная
This paper presents an improved artificial bee colony (IABC) optimization method to solving practical economic dispatch taking into account the nonlinear generator characteristics such as valve-point loading effects. In order to exploit the performance of this new variant based ABC method to solving practical economic dispatch, a new local search mechanism (LSM) associated to the original ABC algorithm; it allows exploiting effectively the promising region to locate the best solution. The proposed approach has been examined and applied to many practical electrical power systems, the 13 generating units, and to the large electrical system with 40 generating units considering valve point loading effects. From the different case studies, it is observed that the results compared with the other recent techniques demonstrate the potential of the proposed approach and show clearly its effectiveness to solve practical and large ED.
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Solving Traveling Salesman Problem Through Genetic Algorithm with Clustering
Статья научная
The Traveling Salesman Problem (TSP) is a well-known NP-hard combinatorial optimization problem, commonly studied in computer science and operations research. Due to its complexity and broad applicability, various algorithms have been designed and developed from the viewpoint of intelligent search. In this paper, we propose a two-stage method based on the clustering concept integrated with an intelligent search technique. In the first stage, a set of clustering techniques - fuzzy c-means (FCM), k-means (KM), and k-mediods (KMD) - are employed independently to generate feasible routes for the TSP. These routes are then optimized in the second stage using an improved Genetic Algorithm (IGA). Actually, we enhance the traditional Genetic Algorithm (GA) through an advanced selection strategy, a new position-based heuristic crossover, and a supervised mutation mechanism (FIB). This IGA is implemented to the feasible routes generated in the clustering stage to search the optimized route. The overall solution approach results in three distinct pathways: FCM+IGA, KM+IGA, and KMD+IGA. Simulation results with 47 benchmark TSP datasets demonstrate that the proposed FCM+IGA performs better than both KM+IGA and KMD+IGA. Moreover, FCM+IGA outperforms other clustering-based algorithms and several state-of-the-art methods in terms of solution quality.
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Solving a Class of Non-Smooth Optimal Control Problems
Статья научная
In this paper, we first propose a new generalized derivative for non-smooth functions and then we utilize this generalized derivative to convert a class of non-smooth optimal control problem to the corresponding smooth form. In the next step, we apply the discretization method to approximate the obtained smooth problem to the nonlinear programming problem. Finally, by solving the last problem, we obtain an approximate optimal solution for main problem.
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Solving a Linear Programming with Fuzzy Constraint and Objective Coefficients
Статья научная
In this paper, we consider a method for solving a linear programming problem with fuzzy objective and coefficient matrix, where the fuzzy numbers are supposed to be triangular. By the proposed method, the Decision Maker will have the flexibility of choosing. The solving method is based on the Pareto algorithm, which converts the problem to a weighted-objective linear programming. For more illustration, after discussing the problem and the algorithm, we present an example, which its solutions are independent from the objective weights.
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Solving school bus routing problem using genetic algorithm-based model
Статья научная
School Bus Routing Problem is an optimization problem which falls under the class of the Vehicle Routing Problem. It involves the use of a fleet of vehicles to efficiently and optimally transport students to and from their schools. To solve this problem, optimal school bus routes are found by minimizing the number of buses, the number of routes and the total distance traversed along all routes. Manual routing of school buses have led to creation of many routes, increased number of buses and several buses navigating the same route, thereby incurring more cost. One of such methods used in solving school bus routing problems is meta-heuristic method which has proven better results in terms of optimal solution and reduced time complexity. In this study, Genetic algorithm is utilized to solve the school bus routing problem because of its simplicity and ability to generate many possible solutions. The algorithm is implemented in C# programming language and tested using secondary data obtained from Ondo State Free-School Bus Shuttle Scheme, Akure, Nigeria. The result shows that of all four nodes (bus stops) used in performance evaluation, Alakure to Oke-Aro junction bus stop presents as the best route which covers a total of 69 nodes with a total distance of 34.5km. This shows that there can be less number of buses in use and reduced number of routes in which the buses are assigned.
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Some Results of Intuitionistic Fuzzy Soft Matrix
Статья научная
The purpose of this article is to consider the notions of intuitionistic fuzzy soft matrices and some basic results. This work deals particularly with the definition of transpose of intuitionistic fuzzy soft matrices and then some properties of transpose of intuitionistic fuzzy soft matrices are studied. After that symmetric intuitionistic fuzzy matrices are also defined and some properties are discussed. Numerical examples are provided to make the concept clear.
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Sorted r-Train: an improved dynamic data structure for handling big data
Статья научная
In today’s computing era, the world is dealing with big data which has enormously expanded in terms of 7Vs (volume, velocity, veracity, variability, value, variety, visualization). The conventional data structures like arrays, linked list, trees, graphs etc. are not able to effectively handle these big data. Therefore new and dynamic tools and techniques which can handle these big data effectively and efficiently are the need of the hour. This paper aims to provide an enhancement to the recently proposed “dynamic” data structure “r-Train” for handling big data. With the emergence of the “Internet of Things (IoT)” technology, real-time handling of requests and services are pivotal. Therefore it becomes necessary to promptly fetch the required data as and when required from the enormous piles of big data that are generally located at different sites. Therefore an effective searching and retrieval mechanism must be provided that can handle these challenging issues. The primary aim of this proposed refinement is to provide an effective means of insertion, deletion and searching techniques to efficiently handle the big data.
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Source Code Author Attribution Using Author's Programming Style and Code Smells
Статья научная
Source code is an intellectual property and using it without author's permission is a violation of property right. Source code authorship attribution is vital for dealing with software theft, copyright issues and piracies. Characterizing author's signature for identifying their footprints is the core task of authorship attribution. Different aspects of source code have been considered for characterizing signatures including author's coding style and programming structure, etc. The objective of this research is to explore another trait of authors' coding behavior for personifying their footprints. The main question that we want to address is that "can code smells are useful for characterizing authors' signatures? A machine learning based methodology is described not only to address the question but also for designing a system. Two different aspects of source code are considered for its representation into features: author's style and code smells. The author's style related feature representation is used as baseline. Results have shown that code smell can improves the authorship attribution.
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Speed up linear scan in high-dimensions by sorting one-dimensional projections
Статья научная
High-dimensional indexing is a pervasive challenge faced in multimedia retrieval. Existing indexing methods applying linear scan strategy, such as VA-file and its variations, are still efficient when the dimensionality is high. In this paper, we propose a new access idea implemented on linear scan based methods to speed up the nearest-neighbor queries. The idea is to map high-dimensional points into two kinds of one-dimensional values using projection and distance computation. The projection values on the line determined by the first Principal Component are sorted and indexed using a B+-tree, and the distances of each point to a reference point are also embedded into leaf node of the B+-tree. When performing nearest neighbor search, the Partial Distortion Searching and triangular inequality are employed to prune search space. In the new search algorithm, only a small portion of data points need to be linearly accessed by computing the bounded distance on the one-dimensional line, which can reduce the I/O and processor time dramatically. Experiment results on large image databases show that the new access method provides a faster search speed than existing high-dimensional index methods.
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Spiking neural network and bull genetic algorithm for active vibration control
Статья научная
Systems with flexible structures display vibration as a characteristic property. However, when exposed to disturbing forces, then the component and/or structural nature of such systems are damaged. Therefore, this paper proposes two heuristics approaches to reduce the unwanted structural response delivered due to the external excitation; namely, bull genetic algorithm and spiking neural network. The bull genetic algorithm is based on a new selection property inherited from the bull concept. On the other hand, spiking neural network possess more than one synaptic terminal between each neural network layer and each synaptic terminal is modelled with a different period of delay. Extensive simulations have been conducted using simulated platform of a flexible beam vibration. To validate the proposed approaches, we performed a qualitative comparison with other related approaches such as traditional genetic algorithm, general regression neural network, bees algorithm, and adaptive neuro-fuzzy inference system. Based on the obtained results, it is found that the proposed approaches have outperformed other approaches, while bull genetic algorithm has a 5.2% performance improvement over spiking neural network.
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Статья научная
Soil cave foundation is a common type in the soil foundation in karst area, the stability of the foundation will be directly affected by the soil cave collapse destruction. Currently, the stability evaluation of soil cave foundation is mostly focus on the quantitative evaluation, such as the collapse mechanism and prevention, while the qualitative assessment is rare. Based on the plastoelasticity theory, firstly analyze the stress state of the soil cave wallsurrounding body in the foundation, distinguish the stress concentration influence area, then improve the soil cave foundation stability computation model by using Molecoulomb strength criterion, finally, take a soil cave foundation stability evaluation as the example in Tongren area, Guizhou, confirming the feasibility and reliability of the improvement model. The influence of the foundation bed size, buried depth, soil cave shape and groundwater level depth was further studied, which reveals the mechanism of the soil cave collapse destruction. And the research indicated that the improved model is feasible, when the foundation bed size is smaller, the buried depth is shallower, the hole shape is more spiky and the groundwater level depth is shallower, the soil cave stability coefficient will be bigger, which is more advantageous to the stability, and the influence of groundwater level depth is more sensitive to the soil cave stability, once the groundwater level depth dropped a little, the stable soil cave will become into failure and instability. Therefore, the quantitative evaluation should be paid more attention in soil cave foundation stability evaluation, particularly under the ground water environment, simultaneously, the calculation results, like soil cave foundation maximum size, the critical buried depth, the maximum water level buried depth, have the strong directive function to the soil cave foundation treatment and design.
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Stage-wise Sieving with Optimized CNN Ensemble for Enhanced ECG Arrhythmia Detection
Статья научная
Accurate detection of ECG arrhythmias plays a critical role in enabling timely diagnosis and treatment of cardiovascular diseases, which remain the leading cause of mortality worldwide. However, achieving high classification performance remains challenging due to class imbalance, signal variability, and resource constraints in real-time deployments. This study aims to enhance ECG arrhythmia detection accuracy through an optimized ensemble approach combining multiple CNN models with a novel stage-wise sieving strategy. Methodology: Three lightweight CNN models (ShuffleNet, MobileNet-v2, ResNet-18) were integrated into a multi-stage binary classification framework. Each stage systematically eliminated accurately classified arrhythmia classes. The novelty of the proposed approach lies in introducing a stage-wise sieving strategy that incrementally removes well-classified classes, combined with an optimized ensemble fusion of multiple CNN models guided by metaheuristic optimization techniques to boost performance. Optimization techniques, including Particle Swarm Optimization, Whale Optimization Algorithm, Grey Wolf Optimizer, Ant Colony Optimization, and Firefly Algorithm, were applied to improve model fusion. The approach was validated using combined public datasets (PTB-XL, MIT-BIH, and Shaoxing ECG databases). Results: The proposed stage-wise sieving ensemble significantly improved overall classification accuracy by 17.95%, reaching 96.29% accuracy using the Grey Wolf Optimizer. Classes previously misclassified, such as Conduction Disturbance and Hypertrophy, exhibited accuracy improvements of up to 32.44% and 25.19%, respectively. Conclusion: The proposed optimized ensemble approach significantly enhances ECG arrhythmia detection performance and demonstrates feasibility for real-time deployment on resource-constrained platforms such as Raspberry Pi.
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Static Security Enhancement and Loss Minimization Using Simulated Annealing
Статья научная
This paper presents a developed algorithm for optimal placement of thyristor controlled series capacitors (TCSC’s) for enhancing the power system static security and minimizing the system overall power loss. Placing TCSC’s at selected branches requires analysis of the system behavior under all possible contingencies. A selective procedure to determine the locations and settings of the thyristor controlled series capacitors is presented. The locations are determined by evaluating contingency sensitivity index (CSI) for a given power system branch for a given number of contingencies. This criterion is then used to develop branches prioritizing index in order to rank the system branches possible for placement of the thyristor controlled series capacitors. Optimal settings of TCSC’s are determined by the optimization technique of simulated annealing (SA), where settings are chosen to minimize the overall power system losses. The goal of the developed methodology is to enhance power system static security by alleviating/eliminating overloads on the transmission lines and maintaining the voltages at all load buses within their specified limits through the optimal placement and setting of TCSC’s under single and double line outage network contingencies. The proposed algorithm is examined using different IEEE standard test systems to shown its superiority in enhancing the system static security and minimizing the system losses.
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Static Timing Analysis of Different SRAM Controllers
Статья научная
Timing-critical path analysis is one of the most significant terms for the VLSI designer. For the formal verification of any kinds of digital chip, static timing analysis (STA) plays a vital role to check the potentiality and viability of the design procedures. This indicates the timing status between setup and holding times required with respect to the active edge of the clock. STA can also be used to identify time sensitive paths, simulate path delays, and assess Register transfer level (RTL) dependability. Four types of Static Random Access Memory (SRAM) controllers in this paper are used to handle with the complexities of digital circuit timing analysis at the logic level. Different STA parameters such as slack, clock skew, data latency, and multiple clock frequencies are investigated here in their node-to-node path analysis for diverse SRAM controllers. Using phase lock loop (ALTPLL), single clock and dual clock are used to get the response of these controllers. For four SRAM controllers, the timing analysis shows that no data violation exists for single and dual clock with 50 MHz and 100 MHz frequencies. Result also shows that the slack for 100MHz is greater than that of 50MHz. Moreover, the clock skew value in our proposed design is lower than in the other three controllers because number of paths, number of states are reduced, and the slack value is higher than in 1st and 2nd controllers. In timing path analysis, slack time determines that the design is working at the desired frequency. Although 100MHz is faster than 50MHz, our proposed SRAM controller meets the timing requirements for 100MHz including the reduction of node to node data delay. Due to this reason, the proposed controller performs well compared to others in terms slack and clock skew.
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Steganographic Data Hiding using Modified APSO
Статья научная
In this paper we are analyzing the steganographic data hiding using the least significant bit technique. This paper describes the particle swarm optimisation. The particle swarm optimisation algorithm is applied to the spatial domain technique. The improved algorithm called the accelerated particle swarm optimisation converges faster than the usual particle swarm optimisation and improves the performance. This paper also analyses the modified particle swarm optimisation on the spatial domain technique which improved the PSNR and also reduced the computation time.
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Step Response Enhancement of Hybrid Stepper Motors Using Soft Computing Techniques
Статья научная
This paper presents the use of different soft computing techniques for step response enhancement of Hybrid Stepper Motors. The basic differential equations of hybrid stepper motor are used to build up a model using MATLAB software package. The implementation of Fuzzy Logic (FL) and Proportional-Integral-Derivative (PID) controllers are used to improve the motor performance. The numerical simulations by a PC-based controller show that the PID controller tuned by Genetic Algorithm (GA) produces better performance than that tuned by Fuzzy controller. They show that, the Fuzzy PID-like controller produces better performance than the other linear Fuzzy controllers. Finally, the comparison between PID controllers tuned by genetic algorithm and the Fuzzy PID-like controller shows that, the Fuzzy PID-like controller produces better performance.
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Stimulate Engagement and Motivation in MOOCs Using an Ontologies Based Multi-Agents System
Статья научная
Today, Massive Open Online Courses (MOOCs) have the potential to enable free online education on an enormous scale. However, a concern often raised about MOOCs is the consistently high drop-out rate of MOOC learners. Although many thousands of learners enroll on these courses, a very small proportion actually complete the course. This work is at the heart of this issue. It is interested in contributing on multi-agents systems and ontologies to describe the learning preferences and adapt educational resources to learner profile in MOOCs platforms. The primary aim of this work is to exploit the potential of multi-agents systems and ontologies to improve learners' engagement and motivation in MOOCs platforms and therefore reduce the drop-out rates. As part of the contribution of this work, the paper proposes a model of Multi-Agent System (MAS), based on ontologies for adapting the learning resources proposed to a learner in a MOOCs platform according to his learning preferences. To model an adequate online course, the determination of learner's preferences is done through the analysis of learner behavior relying on his indicator MBTI (Myers Briggs Type Indicator). The proposed model integrates the main functionalities of an intelligent tutoring system: profiling, updating of the profile, selection, adaptation and presentation of adequate resources. The architecture of the proposed system is composed on two main agents, four ontologies and a set of modules implemented.
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Strategic Sensor Placement for Intrusion Detection in Network-Based IDS
Статья научная
Network Intrusion Detection Systems (NIDSs) can be composed of a potentially large number of sensors, which monitor the traffic flowing in the network. Deciding where sensors should be placed and what information they need in order to detect the desired attacks can be a demanding task for network administrators, one that should be made as automatic as possible. Some few works have been done on positioning sensors using attack graph analysis, formal logic-based approach and Network Simulator NS2 which were studied to determine a strategy for sensors placement on the network. This paper analysed the major considerations for sensors placements, typical sensors deployments in NIDS, and established an extended model for sensors deployment to further strengthen the network for intrusion detection which was based on the escape of some malicious activities through the firewall.
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